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Nobel Laureate Daron Acemoglu: Don't Believe the AI Hype
Daron Acemoglu finds nothing to support widely cited assessments of the technology’s near-term potential.
Early adoption of generative AI has naturally occurred where it performs reasonably well, meaning tasks for which there are objective measures of success, such as writing simple programming subroutines or verifying information. But many of the 4.6% of tasks that could feasibly be automated within ten years – evaluating applications, diagnosing health problems, providing financial advice – do not have such clearly defined objective measures of success, and often involve complex context-dependent variables (what is good for one patient will not be right for another). If you listen to tech industry leaders, business-sector forecasters, and much of the media, you may believe that recent advances in generative AI will soon bring extraordinary productivity benefits, revolutionizing life as we know it.
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